General framework for E(3)-equivariant neural network representation of density functional theory Hamiltonian

• Published in: Nature communications Vol. 14; no. 1; pp. 2848 - 10
• Main Authors: Gong, Xiaoxun, Li, He, Zou, Nianlong, Xu, Runzhang, Duan, Wenhui, Xu, Yong
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 18.05.2023; Nature Publishing Group; Nature Portfolio
• Subjects: 639/301/1034/1037; 639/301/1034/1038; 639/705/117; 639/766/119/995; Accuracy; Deep learning; Density functional theory; Electronic structure; Hamiltonian functions; Humanities and Social Sciences; multidisciplinary; Neural networks; Science; Science (multidisciplinary); Spin-orbit interactions; Structure-function relationships; Symmetry

The combination of deep learning and ab initio calculation has shown great promise in revolutionizing future scientific research, but how to design neural network models incorporating a priori knowledge and symmetry requirements is a key challenging subject. Here we propose an E(3)-equivariant deep-...